Bayesian probability of success for clinical trials using historical data |
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Authors: | Joseph G Ibrahim Ming‐Hui Chen Mani Lakshminarayanan Guanghan F Liu Joseph F Heyse |
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Institution: | 1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A.;2. Department of Statistics, University of Connecticut, Storrs, CT 06269, U.S.A.;3. Biotechnology Clinical Development, Pfizer, Inc., Collegeville, PA 19426, U.S.A.;4. Merck & Co., Inc., Whitehouse Station, NJ 08889, U.S.A. |
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Abstract: | Developing sophisticated statistical methods for go/no‐go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang‐Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre‐market or post‐market trials. Copyright © 2014 John Wiley & Sons, Ltd. |
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Keywords: | average probability of success fitting prior linear regression marginal likelihood power prior simulation validation prior |
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